摘要：這篇文章將講解TensorFlow如何保存變量和神經網絡參數，通過Saver保存神經網絡，再通過Restore調用訓練好的神經網絡。
本文分享自華為雲社區《[Python人工智能] 十一.Tensorflow如何保存神經網絡參數 丨【百變AI秀】》，作者： eastmount。
一.保存變量
通過tf.Variable()定義權重和偏置變量，然后調用tf.train.Saver()存儲變量，將數據保存至本地“my_net/save_net.ckpt”文件中。
# -*- coding: utf-8 -*- """ Created on Thu Jan 2 20:04:57 2020 @author: xiuzhang Eastmount CSDN """ import tensorflow as tf import numpy as np #---------------------------------------保存文件--------------------------------------- W = tf.Variable([[1,2,3], [3,4,5]], dtype=tf.float32, name='weights') #2行3列的數據 b = tf.Variable([[1,2,3]], dtype=tf.float32, name='biases') # 初始化 init = tf.initialize_all_variables() # 定義saver 存儲各種變量 saver = tf.train.Saver() # 使用Session運行初始化 with tf.Session() as sess: sess.run(init) # 保存 官方保存格式為ckpt save_path = saver.save(sess, "my_net/save_net.ckpt") print("Save to path:", save_path)
“Save to path: my_net/save_net.ckpt”保存成功如下圖所示：
打開內容如下圖所示：
接着定義標記變量train，通過Restore操作使用我們保存好的變量。注意，在Restore時需要定義相同的dtype和shape，不需要再定義init。最后直接通過 saver.restore(sess, “my_net/save_net.ckpt”) 提取保存的變量並輸出即可。
# -*- coding: utf-8 -*- """ Created on Thu Jan 2 20:04:57 2020 @author: xiuzhang Eastmount CSDN """ import tensorflow as tf import numpy as np # 標記變量 train = False #---------------------------------------保存文件--------------------------------------- # Save if train==True: # 定義變量 W = tf.Variable([[1,2,3], [3,4,5]], dtype=tf.float32, name='weights') #2行3列的數據 b = tf.Variable([[1,2,3]], dtype=tf.float32, name='biases') # 初始化 init = tf.global_variables_initializer() # 定義saver 存儲各種變量 saver = tf.train.Saver() # 使用Session運行初始化 with tf.Session() as sess: sess.run(init) # 保存 官方保存格式為ckpt save_path = saver.save(sess, "my_net/save_net.ckpt") print("Save to path:", save_path) #---------------------------------------Restore變量------------------------------------- # Restore if train==False: # 記住在Restore時定義相同的dtype和shape # redefine the same shape and same type for your variables W = tf.Variable(np.arange(6).reshape((2,3)), dtype=tf.float32, name='weights') #空變量 b = tf.Variable(np.arange(3).reshape((1,3)), dtype=tf.float32, name='biases') #空變量 # Restore不需要定義init saver = tf.train.Saver() with tf.Session() as sess: # 提取保存的變量 saver.restore(sess, "my_net/save_net.ckpt") # 尋找相同名字和標識的變量並存儲在W和b中 print("weights", sess.run(W)) print("biases", sess.run(b))
運行代碼，如果報錯“NotFoundError: Restoring from checkpoint failed. This is most likely due to a Variable name or other graph key that is missing from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. ”，則需要重置Spyder即可。
最后輸出之前所保存的變量，weights為 [[1,2,3], [3,4,5]]，偏置為 [[1,2,3]]。
二.保存神經網絡
那么，TensorFlow如何保存我們的神經網絡框架呢？我們需要把整個網絡訓練好再進行保存，其方法和上面類似，完整代碼如下：
""" Created on Sun Dec 29 19:21:08 2019 @author: xiuzhang Eastmount CSDN """ import os import glob import cv2 import numpy as np import tensorflow as tf # 定義圖片路徑 path = 'photo/' #---------------------------------第一步 讀取圖像----------------------------------- def read_img(path): cate = [path + x for x in os.listdir(path) if os.path.isdir(path + x)] imgs = [] labels = [] fpath = [] for idx, folder in enumerate(cate): # 遍歷整個目錄判斷每個文件是不是符合 for im in glob.glob(folder + '/*.jpg'): #print('reading the images:%s' % (im)) img = cv2.imread(im) #調用opencv庫讀取像素點 img = cv2.resize(img, (32, 32)) #圖像像素大小一致 imgs.append(img) #圖像數據 labels.append(idx) #圖像類標 fpath.append(path+im) #圖像路徑名 #print(path+im, idx) return np.asarray(fpath, np.string_), np.asarray(imgs, np.float32), np.asarray(labels, np.int32) # 讀取圖像 fpaths, data, label = read_img(path) print(data.shape) # (1000, 256, 256, 3) # 計算有多少類圖片 num_classes = len(set(label)) print(num_classes) # 生成等差數列隨機調整圖像順序 num_example = data.shape[0] arr = np.arange(num_example) np.random.shuffle(arr) data = data[arr] label = label[arr] fpaths = fpaths[arr] # 拆分訓練集和測試集 80%訓練集 20%測試集 ratio = 0.8 s = np.int(num_example * ratio) x_train = data[:s] y_train = label[:s] fpaths_train = fpaths[:s] x_val = data[s:] y_val = label[s:] fpaths_test = fpaths[s:] print(len(x_train),len(y_train),len(x_val),len(y_val)) #800 800 200 200 print(y_val) #---------------------------------第二步 建立神經網絡----------------------------------- # 定義Placeholder xs = tf.placeholder(tf.float32, [None, 32, 32, 3]) #每張圖片32*32*3個點 ys = tf.placeholder(tf.int32, [None]) #每個樣本有1個輸出 # 存放DropOut參數的容器 drop = tf.placeholder(tf.float32) #訓練時為0.25 測試時為0 # 定義卷積層 conv0 conv0 = tf.layers.conv2d(xs, 20, 5, activation=tf.nn.relu) #20個卷積核 卷積核大小為5 Relu激活 # 定義max-pooling層 pool0 pool0 = tf.layers.max_pooling2d(conv0, [2, 2], [2, 2]) #pooling窗口為2x2 步長為2x2 print("Layer0：\n", conv0, pool0) # 定義卷積層 conv1 conv1 = tf.layers.conv2d(pool0, 40, 4, activation=tf.nn.relu) #40個卷積核 卷積核大小為4 Relu激活 # 定義max-pooling層 pool1 pool1 = tf.layers.max_pooling2d(conv1, [2, 2], [2, 2]) #pooling窗口為2x2 步長為2x2 print("Layer1：\n", conv1, pool1) # 將3維特征轉換為1維向量 flatten = tf.layers.flatten(pool1) # 全連接層 轉換為長度為400的特征向量 fc = tf.layers.dense(flatten, 400, activation=tf.nn.relu) print("Layer2：\n", fc) # 加上DropOut防止過擬合 dropout_fc = tf.layers.dropout(fc, drop) # 未激活的輸出層 logits = tf.layers.dense(dropout_fc, num_classes) print("Output：\n", logits) # 定義輸出結果 predicted_labels = tf.arg_max(logits, 1) #---------------------------------第三步 定義損失函數和優化器--------------------------------- # 利用交叉熵定義損失 losses = tf.nn.softmax_cross_entropy_with_logits( labels = tf.one_hot(ys, num_classes), #將input轉化為one-hot類型數據輸出 logits = logits) # 平均損失 mean_loss = tf.reduce_mean(losses) # 定義優化器 學習效率設置為0.0001 optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(losses) #------------------------------------第四步 模型訓練和預測----------------------------------- # 用於保存和載入模型 saver = tf.train.Saver() # 訓練或預測 train = False # 模型文件路徑 model_path = "model/image_model" with tf.Session() as sess: if train: print("訓練模式") # 訓練初始化參數 sess.run(tf.global_variables_initializer()) # 定義輸入和Label以填充容器 訓練時dropout為0.25 train_feed_dict = { xs: x_train, ys: y_train, drop: 0.25 } # 訓練學習1000次 for step in range(1000): _, mean_loss_val = sess.run([optimizer, mean_loss], feed_dict=train_feed_dict) if step % 50 == 0: #每隔50次輸出一次結果 print("step = {}\t mean loss = {}".format(step, mean_loss_val)) # 保存模型 saver.save(sess, model_path) print("訓練結束，保存模型到{}".format(model_path)) else: print("測試模式") # 測試載入參數 saver.restore(sess, model_path) print("從{}載入模型".format(model_path)) # label和名稱的對照關系 label_name_dict = { 0: "人類", 1: "沙灘", 2: "建築", 3: "公交", 4: "恐龍", 5: "大象", 6: "花朵", 7: "野馬", 8: "雪山", 9: "美食" } # 定義輸入和Label以填充容器 測試時dropout為0 test_feed_dict = { xs: x_val, ys: y_val, drop: 0 } # 真實label與模型預測label predicted_labels_val = sess.run(predicted_labels, feed_dict=test_feed_dict) for fpath, real_label, predicted_label in zip(fpaths_test, y_val, predicted_labels_val): # 將label id轉換為label名 real_label_name = label_name_dict[real_label] predicted_label_name = label_name_dict[predicted_label] print("{}\t{} => {}".format(fpath, real_label_name, predicted_label_name)) # 評價結果 print("正確預測個數:", sum(y_val==predicted_labels_val)) print("准確度為:", 1.0*sum(y_val==predicted_labels_val) / len(y_val))
核心步驟為：
saver = tf.train.Saver() model_path = "model/image_model" with tf.Session() as sess: if train: #保存神經網絡 sess.run(tf.global_variables_initializer()) for step in range(1000): _, mean_loss_val = sess.run([optimizer, mean_loss], feed_dict=train_feed_dict) if step % 50 == 0: print("step = {}\t mean loss = {}".format(step, mean_loss_val)) saver.save(sess, model_path) else: #載入神經網絡 saver.restore(sess, model_path) predicted_labels_val = sess.run(predicted_labels, feed_dict=test_feed_dict) for fpath, real_label, predicted_label in zip(fpaths_test, y_val, predicted_labels_val): real_label_name = label_name_dict[real_label] predicted_label_name = label_name_dict[predicted_label] print("{}\t{} => {}".format(fpath, real_label_name, predicted_label_name))
預測輸出結果如下圖所示，最終預測正確181張圖片，准確度為0.905。相比之前機器學習KNN的0.500有非常高的提升。
測試模式
INFO:tensorflow:Restoring parameters from model/image_model 從model/image_model載入模型 b'photo/photo/3\\335.jpg' 公交 => 公交 b'photo/photo/1\\129.jpg' 沙灘 => 沙灘 b'photo/photo/7\\740.jpg' 野馬 => 野馬 b'photo/photo/5\\564.jpg' 大象 => 大象 ... b'photo/photo/9\\974.jpg' 美食 => 美食 b'photo/photo/2\\220.jpg' 建築 => 公交 b'photo/photo/9\\912.jpg' 美食 => 美食 b'photo/photo/4\\459.jpg' 恐龍 => 恐龍 b'photo/photo/5\\525.jpg' 大象 => 大象 b'photo/photo/0\\44.jpg' 人類 => 人類 正確預測個數: 181 准確度為: 0.905